Traffic Congestion Pricing: Methodologies and Equity Implications

Road traffic congestion is recognized as a growing and important urban ill. It occurs in different contexts, takes on many faces and is caused by a variety of processes. It affects both work trips and non-work trips, both passengers and goods flow. It affects the quality of life and the competitiveness of a region. It is an additional cost that arises in the forms of delay, environmental degradation, diminished productivity, standard of living and wasted energy. Congestion pricing can result in winners and losers among different socio-economic groups. However, different studies differ in their conclusions about who wins and who loses because of different assumptions made. This paper reviews the concepts of congestion pricing as a mitigation policy to reduce road congestion and reviews the concept of equity. This paper aims to provide theoretical research that enhances our understanding of congestion pricing policy and the equity implications of this policy

Equity Implications of Cordon Pricing in Downtown Toronto

The City of Toronto has done much to reduce congestion through transportation system management and travel demand measures. Yet, while measures to eliminate the traffic congestion problem have been necessary, they simply have not been sufficient to accommodate over 2.5 million residents and the many more who find their way into the area from points beyond particularly from other regions in the Greater Toronto Area (GTA). In addition, the transportation improvements certainly do not provide capacity adequate to address the needs of the future predicted residents and added economic activity. Congestion pricing is an untapped transportation strategy that can reduce traffic congestion, improve air quality, and raise the revenue essential to implement needed transportation measures that are effective in improving transportation services and facilities. While experience with congestion pricing is limited, there are sufficient examples and experiences around the world to demonstrate that, when implemented properly, it virtually never fails to be an effective tool to curb congestion. Yet, when initially proposed, it never fails to be controversial. This is due in part to the lack of research on the equity impacts on different socio-economic groups. This is the dichotomy and the dilemma of congestion pricing that every city must face in seeking this new approach to congestion management. The main goal of the research is to provide empirical research that enhances our understanding of the equity implications of cordon pricing for the urban region of Toronto, Canada. Three research objectives are identified to address the research goal. The first objective is to examine the ways that the GTA is moving toward or away the principles of sustainable transportation, and thus to make a case that Downtown Toronto is a candidate for cordon pricing. The second objective is to investigate if particular socio-economic groups would be disproportionately affected by the implementation of cordon pricing in Downtown Toronto, as one way of approaching the equity dimensions of such a policy. The third objective is to explore some of the policy aspects associated with implementing cordon pricing in Toronto, including public perceptions of such a policy as well as probable responses to the policy. The major findings of this analysis are that the GTA is not moving in the direction of sustainable transportation, which provides a concrete justification for demand-management interventions and that Downtown Toronto is a candidate for cordon pricing. A Downtown Toronto cordon pricing scheme would be progressive in its effects on the various socio-economic groups, and that the progressivity holds up even when travel is disaggregated by demographic factors such as age, gender, household size and occupational category. Full-time workers account for a larger proportion of the affected trips and the percentage of trips that would be affected is highest for those in the full-time high-income neighborhoods. The analyses show that toll charge is an important factor that would trigger some income groups to change their travel behaviour. People from high-income neighborhoods are more willing to pay the charges and drive as usual than people from other income neighborhoods. Revenue redistribution is critical to assess and achieve equity of congestion pricing

A Comprehensive Machine and Deep Learning Approach for Aerosol Optical Depth Forecasting: New Evidence from the Arabian Peninsula

Accurate forecasting of environmental pollution indicators holds significant importance in diverse fields, including climate modeling, environmental monitoring, and public health. In this study, we investigate a wide range of machine learning and deep learning models to enhance Aerosol Optical Depth (AOD) predictions for the Arabian Peninsula (AP) region, one of the world’s main dust source regions. Additionally, we explore the impact of feature extraction and their different types on the forecasting performance of each of the proposed models. Preprocessing of the data involves inputting missing values, data deseasonalization, and data normalization. Subsequently, hyperparameter optimization is performed on each model using grid search. The empirical results of the basic, hybrid and combined models revealed that the convolutional long short-term memory and Bayesian ridge models significantly outperformed the other basic models. Moreover, for the combined models, specifically the weighted averaging scheme, exhibit remarkable predictive accuracy, outperforming individual models and demonstrating superior performance in longer-term forecasts. Our findings emphasize the efficacy of combining distinct models and highlight the potential of the convolutional long short-term memory and Bayesian ridge models for univariate time series forecasting, particularly in the context of AOD predictions. These accurate daily forecasts bear practical implications for policymakers in various areas such as tourism, transportation, and public health, enabling better planning and resource allocation.Open Access funding provided by the Qatar National Library. This publication was made possible by an NPRP award [NPRP13S0206-200272] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. Open Access funding provided by the Qatar National Library

The impact of COVID-19 pandemic on electricity consumption and electricity demand forecasting accuracy: Empirical evidence from the state of Qatar

The goal of this study is to use machine-learning (ML) techniques and empirical big data to examine the influence of the COVID-19 pandemic on electricity usage and electricity demand forecasting accuracy in buildings in Qatar over time and across sectors. Furthermore, this study statistically investigates the relationship between building electricity consumption and the number of daily infected cases in the State of Qatar. The effect of the pandemic on electricity usage was quantified during various periods of the pandemic years. Around 1 million electricity meter readings per year were considered for six different types of building usage between the years 2010 and 2021. The findings indicate that there was a gap between the actual and simulated electricity consumption during the pandemic years. Furthermore, the results show that the fluctuation in electricity consumption was correlated with the number of daily infected cases in some socioeconomic sectors. The changes in the pattern of electricity consumption during the pandemic years (2020–2021) affected the accuracy of the ML models in predicting electricity consumption in 2022.This publication was made possible by an NPRP award [ NPRP13S-0206-200272 ] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The open access publication of this article was funded by the Qatar National Library (QNL)

Spatial Assessment of COVID-19 First-Wave Mortality Risk in the Global South

The coronavirus disease (COVID-19) that appeared in 2019 gave rise to a major global health crisis that is still topping global health, socioeconomic, and intervention program agendas. Although the outbreak of COVID-19 has had substantial and devastating impacts on developed countries, the countries of the Global South share a higher proportion of the epidemic’s effects as shown particularly in morbidity and mortality rates in low-income countries. Modeling the effects of underlying factors and disease mortality is essential to plan effective control strategies for disease transmission and risks. The relationship between COVID-19 mortality rates and sociodemographic and health determinants can highlight various epidemic fatality risks. In this research, geographic information systems (GIS) and a multilayer perceptron (MLP) artificial neural network (ANN) were adopted to model and examine variations in COVID-19 mortality rates in the Global South. The model’s performance was tested using statistical measures of mean square error (MSE), root mean square error (RMSE), mean bias error (MBE), and the coefficient of determination (R2). The findings indicated that the most important variables in explaining spatial mortality rate variations were the size of the elderly (sixty-five and older) population, accessibility to handwashing facilities, and hospital beds per 1,000 population. Mapping the explanatory variables and estimated mortality rates and determining the importance of each variable in explaining the spatial variation of COVID-19 death rates across countries of the Global South can shed light on how public health care and demographic structures can offer policymakers invaluable guidelines to planning effective intervention strategies.Open Access funding was provided by the Qatar National Library

Simulation and impact analysis of behavioral and socioeconomic dimensions of energy consumption

Human-oriented factors present unavoidable challenges and uncertainties in building energy strategic planning. The uncertainties escalate when the target society is not fully known to the decision-maker and can create performance gaps between the expected and actual outcomes of sustainability targets. This article aims to investigate the role of socioeconomic and behavioral dimensions in residential energy consumption patterns among regions that host high proportions of migrant communities with diverse cultural and ethnic traits. This study evaluates the patterns in human-building interactions and energy behaviors among local and migrant communities based on empirical evidence and survey analysis. The survey data are investigated via machine learning approaches to identify the interdependencies between and feature importance of critical factors that influence human-building interactions and to determine elements that help to discern the energy behavior of locals and migrants. A simulation analysis is conducted to analyze residential energy consumption under different human indoor thermal comfort preferences in multiple case scenarios to demonstrate how improvements in human-building interaction can create saving opportunities. The findings capture the main socioeconomic and behavioral contributors in residential energy consumption and demonstrate the impact of human factor at a high level in regions with imbalanced demographics and societies in transition

Motivation, preference, socioeconomic, and building features: New paradigm of analyzing electricity consumption in residential buildings

In strategic energy planning, human-oriented factors are uncertain and lead to unpredictable challenges. Thus, decision-makers must contextualize the target society to address these uncertainties. More precisely, uncertainties lead to performance gaps between assumed and actual sustainability target outcomes. This study proposed a new framework that considers vital elements, including occupant motivation, preference, socioeconomic characteristics, and building features (MPSEB). To utilize this model, a thorough face-to-face survey questionnaire was administered to measure these elements. This study explored how these elements affect the patterns of residential energy consumption in a region with numerous expat communities of various ethnic and cultural backgrounds. In particular, the study investigated the patterns of energy behaviors and human-building interactions among the residents of Qatar by collecting empirical evidence and conducting a subsequent survey analysis. Machine learning approaches were employed to explore the survey data and determine the interdependencies between features, as well as the significance of the fundamental factors influencing human-building interactions. The XGBoost method was used to conduct a feature importance analysis to determine factors contributing to residential energy consumption. The results revealed the primary behavioral and socioeconomic factors that affect residential energy consumption, and confirmed the influence of human factors in Qatar while considering its diverse population.This publication was made possible by an NPRP award [ NPRP13S-0206–200272 ] from the Qatar National Research Fund (a member of Qatar Foundation ). The statements made herein are solely the responsibility of the authors. The open access publication of this article was funded by the Qatar National Library (QNL)

Accelerating the Change to Smart Societies- a Strategic Knowledge-Based Framework for Smart Energy Transition of Urban Communities

Urban communities differ in their social, economic, and environmental characteristics, as well as in the approach to energy use. Dynamic energy use and available on-site resources allow interaction with the surroundings and contribute to the key performance indicators of smart cities. This study aimed at proposing systematically a strategic framework for smart cities development by gradually transforming urban communities into smart-energy systems. This framework is based on multidisciplinary practices regarding the staged planning of smart communities and develops smart transformation concepts to enhance capacities toward the preservation, revitalization, livability, and sustainability of a community. In this study, we focused on the concept of smart and zero-carbon communities by using technology and infrastructure. We also considered the premise of the “community” and the related social, technological, and economic aspects. The decision constructs are explained from the perspective of a bottom-up approach ranging from preliminary inspections to economic investment planning. The study proposed a set of decision constructs aimed at allowing planners, engineers, and investors to have different alternatives at their disposal and select a feasible set of practical solutions for smart transformations accordingly.This publication was made possible by an NPRP awards (NPRP11S-1228-170,142 and NPRP13S-0206-200272) from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are the sole responsibility of the authors. The publication of this article was funded by the Qatar National Library

Remote sensing-based assessment of mangrove ecosystems in the Gulf Cooperation Council countries: a systematic review

Mangrove forests in the Gulf Cooperation Council (GCC) countries are facing multiple threats from natural and anthropogenic-driven land use change stressors, contributing to altered ecosystem conditions. Remote sensing tools can be used to monitor mangroves, measure mangrove forest-and-tree-level attributes and vegetation indices at different spatial and temporal scales that allow a detailed and comprehensive understanding of these important ecosystems. Using a systematic literature approach, we reviewed 58 remote sensing-based mangrove assessment articles published from 2010 through 2022. The main objectives of the study were to examine the extent of mangrove distribution and cover, and the remotely sensed data sources used to assess mangrove forest/tree attributes. The key importance of and threats to mangroves that were specific to the region were also examined. Mangrove distribution and cover were mainly estimated from satellite images (75.2%), using NDVI (Normalized Difference Vegetation Index) derived from Landsat (73.3%), IKONOS (15%), Sentinel (11.7%), WorldView (10%), QuickBird (8.3%), SPOT-5 (6.7%), MODIS (5%) and others (5%) such as PlanetScope. Remotely sensed data from aerial photographs/images (6.7%), LiDAR (Light Detection and Ranging) (5%) and UAV (Unmanned Aerial Vehicles)/Drones (3.3%) were the least used. Mangrove cover decreased in Saudi Arabia, Oman, Bahrain, and Kuwait between 1996 and 2020. However, mangrove cover increased appreciably in Qatar and remained relatively stable for the United Arab Emirates (UAE) over the same period, which was attributed to government conservation initiatives toward expanding mangrove afforestation and restoration through direct seeding and seedling planting. The reported country-level mangrove distribution and cover change results varied between studies due to the lack of a standardized methodology, differences in satellite imagery resolution and classification approaches used. There is a need for UAV-LiDAR ground truthing to validate country-and-local-level satellite data. Urban development-driven coastal land reclamation and pollution, climate change-driven temperature and sea level rise, drought and hypersalinity from extreme evaporation are serious threats to mangrove ecosystems. Thus, we encourage the prioritization of mangrove conservation and restoration schemes to support the achievement of related UN Sustainable Development Goals (13 climate action, 14 life below water, and 15 life on land) in the GCC countries

Spatiotemporal analysis of water-electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors in Doha City, Qatar

This study investigates the water – electricity consumption in the context of the COVID-19 pandemic across six socioeconomic sectors. Due to inadequate research on spatial modelling of water – electricity consumption in the context of the COVID-19 pandemic, this study investigated geographical block-level variation in water and electricity consumption in Doha city of Qatar. Spatial analyses were performed to investigate the spatial differences in each sector. Five geospatial techniques in a Geographical Information System (GIS) context were used in the study. Moran’s I, Anselin Local Moran’s I, and Getis-Ord G* i statistics tools were used to identify the hot spots and cold spots of water and electricity consumption in each sector. Furthermore, Ordinary Least Square (OLS) and Geographically Weighted Regression (GWR) models were employed to investigate the spatial relationship between water and electricity consumption during the pandemic year. The findings show that there is a distinction in water and electricity consumption at the block level across all sectors and over time. Hot spot and spatial regression analysis reveal spatial and temporal heterogeneities in the study area across the six socioeconomic sectors. The intensity of hot spots of water and electricity consumption are found in the southern and western parts of the city due to high population density and the concentration of the commercial and industrial areas. Furthermore, analyzing the spatiotemporal correlation between the water and electricity consumption across the six sectors shows variation within and between these sectors over space and time. The results show a positive relationship between water and electricity consumption in some blocks and over time of each sector. During the lockdown phase, strong positive correlation between water and electricity consumption have exist in the residential sector due to extra water and electricity footprints in this sector. Conversely, the water and electricity consumption were positively correlated but declined in the industrial and commercial sector due to the curtailment in production, economic activities, and reduction in people’s mobility. Mapping the hot spot blocks and the blocks with high relationship between water and electricity consumption could provide useful insight to decision-makers for targeted interventions.This publication was made possible by an NPRP award [ NPRP13S-0206-200272 ] from the Qatar National Research Fund (a member of Qatar Foundation). The statements made herein are solely the responsibility of the authors. The open access publication of this article was funded by the Qatar National Library (QNL) .Scopu